{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sensitivity of the subbasin size on discharge" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example shows how to change the subbasin size repeatedly using the builtin GRASS interface and investigate the changes in discharge of each configuration. " ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "nbsphinx": "hidden" }, "outputs": [], "source": [ "# hidden cell to setup\n", "import swimpy, os\n", "%matplotlib inline\n", "\n", "project_path = os.path.join(os.path.dirname(swimpy.__file__), '../tests/project')\n", "os.chdir(project_path)\n", "if not os.path.exists('swimpy'):\n", " p = swimpy.project.setup()\n", " p.browser.settings.unset()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Upthresh 20 -> mean subbasin size 48.2 (n=21)\n", "Upthresh 50 -> mean subbasin size 126.5 (n=8)\n", "Upthresh 200 -> mean subbasin size 337.4 (n=3)\n" ] } ], "source": [ "import os\n", "import pandas as pd\n", "from matplotlib import pyplot as plt\n", "\n", "import swimpy\n", "\n", "grass_settings = dict(grass_db='../../tests/grassdb',\n", " grass_location='utm32n',\n", " grass_mapset='swim',\n", " grass_setup=dict(elevation=\"elevation@PERMANENT\",\n", " stations=\"stations@PERMANENT\",\n", " landuse=\"landuse@PERMANENT\",\n", " soil=\"soil@PERMANENT\",\n", " upthresh=40,\n", " lothresh=11)\n", " )\n", "\n", "p = swimpy.Project()\n", "\n", "# work on a clone to avoid messing things up\n", "c = p.clone('subbasin_size_sensitivity', **grass_settings)\n", "\n", "# r.watershed threshold values\n", "upthresh = [20, 50, 200]\n", "# get clim file averages\n", "mclim = c.climate.inputdata.mean(axis=1, level=0)\n", "\n", "q = pd.DataFrame()\n", "for ut in upthresh:\n", " # run m.swim.subbasins and necessary postprocesses\n", " c.subbasins.update(upthresh=ut)\n", " # get mean subbasin size and count\n", " sb_size = c.subbasins.attributes['size']\n", " nsb = len(sb_size)\n", " # put mean values and right number of subbasins into clim files\n", " nclim = pd.DataFrame({(v, i+1): mclim[v] for v in mclim.columns for i in range(nsb)})\n", " c.climate.inputdata(nclim)\n", " # run SWIM\n", " c.run(save=False, quiet=True)\n", " # keep Q at Blankenstein\n", " label = '%1.1f (n=%i)' % (sb_size.mean(), nsb)\n", " q[label] = c.station_daily_discharge['BLANKENSTEIN']\n", " print('Upthresh %s -> mean subbasin size %s' % (ut, label))\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualise results\n", "ax = q['1992-03-01':'1992-03-31'].plot()\n", "plt.title('Sensitivity of subbasin size at Blankenstein')\n", "yl = plt.ylabel('Discharge [m^2s^-1]') " ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.14" } }, "nbformat": 4, "nbformat_minor": 2 }